Quan Shi | Search arXiv

arXiv2026

ZK-eSIM: A Privacy-Centric Zero-Knowledge Approach for eSIM Provisioning

GSMA Remote SIM Provisioning (RSP) enables over-the-air delivery of eSIM profiles, but it exposes long-lived identifiers during profile ordering and download. In particular, stable device identifiers (e.g., EID), profile identifiers, and long-lived certificate material enable mobile operators and profile-delivery infrastructure to link provisioning events to the same eUICC and, when combined with account records, to the same subscriber. This undermines subscriber anonymity and enables cross-session tracking. We present ZK-eSIM, a privacy-preserving redesign that achieves subscriber anonymity and provisioning-session unlinkability while retaining accountable traceability by exception. ZK-eSIM (i) replaces direct disclosure of device identifiers with a zero-knowledge proof of device validity and eligibility; (ii) enforces session unlinkability through short-lived, one-time pseudonymous credentials and per-session identifiers to prevent cross-session tracking; and (iii) provides privacy-preserving accountable traceability through a jointly authorised escrow mechanism, so that no single entity can unilaterally deanonymise a user. We formalise a multi-entity, honest-but-curious threat model and prove subscriber anonymity and the unlinkability of provisioning sessions under standard cryptographic assumptions. We implement a Java Card applet on a test eUICC to evaluate performance on commodity hardware with a modified LPA and SM-DP+ server. Our experiments quantify end-to-end cryptographic overhead relative to conventional RSP, confirming that ZK-eSIM adds only practical overhead, closing a critical privacy gap while preserving deployability within existing GSMA roles and interfaces.

cs.CR↗

arXiv2026

$τ^τ$-Bench: An Environment for End-To-End, Realistic Agent Construction

LLM agents are rapidly becoming production software, deployed to handle customer service, adjudicate disputes, and operate internal systems. Notably, the work of building them is increasingly handed to coding agents, yet existing benchmarks say little about whether an AI system can deliver one under the conditions of a real client engagement. We introduce $τ^τ$-bench (pronounced hyper-tau-bench), a benchmark that makes agent construction the task. A developer agent is given the records a business actually keeps, a client who holds requirements, a production API that operations must run through, a codebase to inherit, and limits on serving cost and models: the same starting point a real engagement provides. From these it must deliver a complete customer-service agent, scored by deploying that agent against held-out simulated users. Across 53 tasks spanning four domains, the strongest configuration, Claude Opus 5 under Claude Code, passes just 23.9% of evaluation simulations. Meanwhile, an expert-authored reference ceiling scores 82.2%. The failures mirror ones human agent developers see: models issue shallow queries in place of deep comprehension of the records, communicate almost nothing to the client, and experiment too little with agent architecture and serving spend, shipping the first design that runs. We aim for $τ^τ$-bench to turn the work of cooperative agent building into a measurable target for coding agents.

cs.AI↗